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Title: Quantum-Assisted Generative AI for Simulation of the Calorimeter Response
As CERN approaches the launch of the High Luminosity Large Hadron Collider (HL-LHC) by the decade’s end, the computational demands of traditional simulations have become untenably high. Projections show millions of CPU-years required to create simulated datasets - with a substantial fraction of CPU time devoted to calorimetric simulations. This presents unique opportunities for breakthroughs in computational physics. We show how Quantumassisted Generative AI can be used for the purpose of creating synthetic, realistically scaled calorimetry dataset. The model is constructed by combining D-Wave’s Quantum Annealer processor with a Deep Learning architecture, increasing the timing performance with respect to first principles simulations and Deep Learning models alone, while maintaining current state-of-the-art data quality  more » « less
Award ID(s):
2212550
PAR ID:
10651659
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ;
Editor(s):
Szumlak, T; Rachwał, B; Dziurda, A; Schulz, M; vom_Bruch, D; Ellis, K; Hageboeck, S
Publisher / Repository:
EDP Sciences
Date Published:
Journal Name:
EPJ Web of Conferences
Volume:
337
ISSN:
2100-014X
Page Range / eLocation ID:
01233
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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